Forward-deployed data & AI engineering.One workflow at a time, into production.
We embed senior engineers with your team, take one operational workflow from your existing enterprise data platform to a production AI system in weeks, and hand it over running.
PartnerMicrosoft Fabric (OneLake)Azure · AWS · GCPModel-neutralThe architecture every deployment shares
Not a strategy deck. Not a migration. Not engineers by the hour.
Our senior engineers sit directly with the people who run the workflow, connect the data you already have, build the system, put it into production, and leave you running it.
- Hands-on from day one
Senior-Led Pods
Every pod is led by engineers who have designed, built and run enterprise data platforms in production: lakehouse architecture, governance, integration and applied AI. You work directly with the people building your system.
- No owner, no start
Named Domain Owner
The customer names the domain owner of the workflow before we start. That person approves outputs, defines success and is in the room at every milestone.
- No licence to renew
Handover Without Lock-In
Every line of pipeline logic, schema declaration, prompt evaluation harness, and documentation lives inside your repositories and runs on your compute.
Three workflows we take to production.
We don’t stop at prototypes. We take high-friction operational processes into production and measure the return against the baseline.
- Workflow 01Extraction & Decision
Document-to-decision
Invoices, claims, KYC files, contracts, compliance filings. Extraction into governed tables, an agent that assembles the case and proposes the decision, human approval with a full audit trail.
Governed lakehouse schemaDeterministic validation rules + LLM confidence threshold routing.
Measured production KPIs- Hours per document
- ↓ Lower
- Cycle time
- ↓ Shorter
- Straight-through rate
- ↑ Higher
- Workflow 02Triage & Resolution
Cross-system exception resolution
Failed orders, shipments, work orders, payments. Exceptions land in the lakehouse; an agent triages, pulls context from ERP, WMS and CRM, proposes a resolution, and routes anything outside policy to a person.
Contextual multi-system graphContext from ERP, WMS and CRM assembled into one case timeline.
Measured production KPIs- Mean time to resolve
- ↓ Shorter
- Auto-resolved in policy
- ↑ Higher
- Operational backlog
- ↓ Smaller
- Workflow 03Bounded Copilot
Governed knowledge workflow
For a support, field-service or compliance team. A governed knowledge layer with access control, an assistant inside the team's own tool that answers, drafts and executes bounded actions — with evals, logging and transparency built in.
Strict RBAC + continuous evalsAnswers grounded in governed sources, checked by evals on every release.
Measured production KPIs- Handle time
- ↓ Shorter
- First-time-right
- ↑ Higher
- Escalation rate
- ↓ Lower
Five stages. Production is the finish line.
Prototype doesn’t count. Running production software does.
- Stage 011–5 days · free
Exploration Days
Pick the workflow, capture the baseline, build a working slice where access allows. You leave with a verified baseline metric and a priced Lab proposal.
Deliverable: priced Lab proposal - Stage 023 weeks · credited
Workflow Lab
A working prototype on real customer data, directly in your cloud tenant, with the workflow domain owner actively in the room validating business assertions.
Credited against sprint fee - Stage 03 · Core8–10 weeks · fixed fee
Production Sprint
Integration, model evaluations, human-in-the-loop approval UI, audit logs, operational monitoring, and full handover documentation. Adoption is certified by the domain owner.
Domain owner certification - Stage 046–12 months · optional
Expansion Pod
The dedicated senior engineering pod continues onto adjacent operational workflows, priced on a predictable recurring engineering fee plus verified outcome bonuses.
Adjacent pipelines scaled - Stage 05Your team, from here
Handover: Your Team Runs It
All code in your repositories, complete operational runbooks in your hands, and your internal engineering teams thoroughly trained and extending what we built.
Code · runbooks · trained team
True, and checkable.
Every deployment is measured against the baseline taken during the Exploration Days. No vanity metrics.
- 01 // Independence
Model-neutral, multi-cloud
Claude, OpenAI or open-weight models, chosen by evaluation against your data, on Azure, AWS or GCP. No proprietary middle layer.
- 02 // Compliance
Evals & audit logs from day one
EU AI Act transparency duties apply now; high-risk duties apply from December 2027. Evals, logging and human oversight are built in from the first sprint.
- 03 // Presence
On site with your team
Across the Netherlands, and in the Nordics by arrangement — in the room with the people who run the workflow.
Where we’ve worked:
Inside financial services, insurance, leasing and telecom groups in the Netherlands, Germany and Switzerland — based directly from Amsterdam.
- Financial services
- Insurance
- Leasing & mobility
- Telecom & digital
One technical post per deployment.
The architecture, the evals, the before-and-after KPI — published with the client’s permission.
[ First post follows the first Production Sprint ]
Bring one workflow.
We’ll spend one to five days on it.
Free, on site, and gated: a named workflow, a domain owner who attends, and data access that week. You leave with a measured baseline, a working slice where access allows, and a priced Lab proposal.
Three questions before we book
- 1The workflow
Operational, recurring, high-friction, and currently manual or error-prone.
- 2The domain owner
The operational leader who knows the edge-cases, attends, and validates outputs.
- 3The data platform
Credentials, Lakehouse/Fabric sandbox, and permissions granted for that week.
We book once all three are in place